Abstract
Spectroscopic methods in tandem with machine learning methodologies have attracted considerable research interest for the estimation of food quality. The objective of this study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) coupled with appropriate machine learning regression algorithms for assessing meat microbiological quality. For this purpose, minced pork patties were stored aerobically and under modified atmosphere packaging (MAP) conditions, at isothermal and dynamic temperature conditions. At regular time intervals during storage, samples were subjected to (i) microbiological analysis, (ii) FTIR measurements and (iii) MSI acquisition. The collected FTIR data were processed by feature extraction methods to reduce dimensionality, and subsequently Support Vector Machines (SVM) regression models were trained using spectral features (FTIR and MSI) to estimate microbiological quality of meat (microbial population). The regression models were evaluated with different experimental replicates using distinct meat batches. The performance of the models was evaluated in terms of correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The RMSE values for the microbial population estimation models using FTIR were 1.268 and 1.024 for aerobic and MAP storage, respectively. The performance in terms of RMSE for the MSI-based models was 1.144 for aerobic and 0.923 for MAP storage, while the combination of FTIR and MSI spectra resulted in models with RMSE equal to 1.146 for aerobic and 0.886 for MAP storage. The experimental results demonstrated the potential of estimating the microbiological quality of minced pork meat from spectroscopic data.
Highlights
In the current Food Safety approach, a wide range of audits and inspections are applied to evaluate the quality or safety of raw or processed materials and food products [1]
Spectral data obtained from Fourier transform infrared (FTIR) spectroscopy in the range of 1800 to 900cm−1 and from multispectral imaging (MSI) in the range of 405 to 970 nm were used as X-variables for the development of the regression models
A moderate linear relationship can be seen in all methods with Principal Component Analysis (PCA), Partial Least Squares (PLS) and ReliefF methods presenting stronger relationship than descriptive statistics and Autoencoders, which is in agreement with the results of Table 3
Summary
In the current Food Safety approach, a wide range of audits and inspections are applied to evaluate the quality or safety of raw or processed materials and food products [1]. This is largely based on good design of processes, products and procedures, where the end or finished product testing is considered to be the control measure of the production process. Chemical analyses are used to monitor safety and quality of foods These analyses have certain disadvantages, as they are (i) time-consuming in providing retrospective results, (ii) costly, (iii) some require high-tech molecular tools and experienced and specialized personnel and commonly (iv) destructive to test products, limiting their potential to be used on-, in- or
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